Overview

Dataset statistics

Number of variables16
Number of observations418
Missing cells826
Missing cells (%)12.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.4 KiB
Average record size in memory128.3 B

Variable types

Numeric15
Categorical1

Warnings

df_index is highly correlated with Unnamed: 0High correlation
Unnamed: 0 is highly correlated with df_indexHigh correlation
Friday has 30 (7.2%) missing values Missing
Monday has 59 (14.1%) missing values Missing
Saturday has 96 (23.0%) missing values Missing
Sunday has 162 (38.8%) missing values Missing
Thursday has 112 (26.8%) missing values Missing
Tuesday has 11 (2.6%) missing values Missing
Wednesday has 85 (20.3%) missing values Missing
ncoachingid has 106 (25.4%) missing values Missing
total coaching improved has 162 (38.8%) missing values Missing
df_index has unique values Unique
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2021-05-13 18:56:02.490545
Analysis finished2021-05-13 18:56:45.360257
Duration42.87 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.4641148
Minimum3
Maximum488
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:45.462360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29.85
Q1125.25
median250
Q3374.75
95-th percentile465.15
Maximum488
Range485
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation141.3334405
Coefficient of variation (CV)0.5688283822
Kurtosis-1.236389434
Mean248.4641148
Median Absolute Deviation (MAD)125
Skewness-0.01755172389
Sum103858
Variance19975.1414
MonotocityStrictly increasing
2021-05-13T13:56:45.678841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31
 
0.2%
3751
 
0.2%
3381
 
0.2%
3371
 
0.2%
3361
 
0.2%
3351
 
0.2%
3341
 
0.2%
3331
 
0.2%
3321
 
0.2%
3311
 
0.2%
Other values (408)408
97.6%
ValueCountFrequency (%)
31
0.2%
41
0.2%
71
0.2%
81
0.2%
101
0.2%
ValueCountFrequency (%)
4881
0.2%
4871
0.2%
4861
0.2%
4851
0.2%
4841
0.2%

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean248.4641148
Minimum3
Maximum488
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:45.835294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29.85
Q1125.25
median250
Q3374.75
95-th percentile465.15
Maximum488
Range485
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation141.3334405
Coefficient of variation (CV)0.5688283822
Kurtosis-1.236389434
Mean248.4641148
Median Absolute Deviation (MAD)125
Skewness-0.01755172389
Sum103858
Variance19975.1414
MonotocityStrictly increasing
2021-05-13T13:56:45.999923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31
 
0.2%
3751
 
0.2%
3381
 
0.2%
3371
 
0.2%
3361
 
0.2%
3351
 
0.2%
3341
 
0.2%
3331
 
0.2%
3321
 
0.2%
3311
 
0.2%
Other values (408)408
97.6%
ValueCountFrequency (%)
31
0.2%
41
0.2%
71
0.2%
81
0.2%
101
0.2%
ValueCountFrequency (%)
4881
0.2%
4871
0.2%
4861
0.2%
4851
0.2%
4841
0.2%

employeeid
Real number (ℝ≥0)

Distinct273
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26768058.64
Minimum26702507
Maximum35706317
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:46.192516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum26702507
5-th percentile26702701.15
Q126703200.75
median26703526
Q326703807
95-th percentile26703958.45
Maximum35706317
Range9003810
Interquartile range (IQR)606.25

Descriptive statistics

Standard deviation760868.2483
Coefficient of variation (CV)0.02842448377
Kurtosis135.9758747
Mean26768058.64
Median Absolute Deviation (MAD)294.5
Skewness11.71858564
Sum1.118904851 × 1010
Variance5.789204912 × 1011
MonotocityIncreasing
2021-05-13T13:56:46.407889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
267037003
 
0.7%
267032203
 
0.7%
267033023
 
0.7%
267038973
 
0.7%
267038183
 
0.7%
267033153
 
0.7%
267033783
 
0.7%
267033083
 
0.7%
267031943
 
0.7%
267035533
 
0.7%
Other values (263)388
92.8%
ValueCountFrequency (%)
267025072
0.5%
267025372
0.5%
267025492
0.5%
267025941
0.2%
267026111
0.2%
ValueCountFrequency (%)
357063172
0.5%
357063051
0.2%
267039831
0.2%
267039822
0.5%
267039791
0.2%

Friday
Real number (ℝ≥0)

MISSING

Distinct139
Distinct (%)35.8%
Missing30
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean95.26657223
Minimum67.8575
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:46.619010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum67.8575
5-th percentile83.33375
Q193.334
median96.186
Q3100
95-th percentile100
Maximum100
Range32.1425
Interquartile range (IQR)6.666

Descriptive statistics

Standard deviation5.3706653
Coefficient of variation (CV)0.05637512902
Kurtosis4.677514818
Mean95.26657223
Median Absolute Deviation (MAD)3.814
Skewness-1.862301056
Sum36963.43002
Variance28.84404577
MonotocityNot monotonic
2021-05-13T13:56:46.832536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100120
28.7%
93.757
 
1.7%
93.3346
 
1.4%
956
 
1.4%
905
 
1.2%
95.833333335
 
1.2%
95.238571435
 
1.2%
98.43754
 
1.0%
94.4454
 
1.0%
87.54
 
1.0%
Other values (129)222
53.1%
(Missing)30
 
7.2%
ValueCountFrequency (%)
67.85752
0.5%
752
0.5%
77.781
 
0.2%
78.3343
0.7%
79.113751
 
0.2%
ValueCountFrequency (%)
100120
28.7%
99.038752
 
0.5%
98.863751
 
0.2%
98.718333333
 
0.7%
98.611252
 
0.5%

Monday
Real number (ℝ≥0)

MISSING

Distinct139
Distinct (%)38.7%
Missing59
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean94.40371216
Minimum70.8325
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:47.056778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum70.8325
5-th percentile83.334875
Q191.66666667
median95.3125
Q3100
95-th percentile100
Maximum100
Range29.1675
Interquartile range (IQR)8.333333333

Descriptive statistics

Standard deviation5.534483123
Coefficient of variation (CV)0.0586256938
Kurtosis1.984383172
Mean94.40371216
Median Absolute Deviation (MAD)3.64625
Skewness-1.286373458
Sum33890.93267
Variance30.63050344
MonotocityNot monotonic
2021-05-13T13:56:47.263994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10092
22.0%
96.296666678
 
1.9%
87.57
 
1.7%
96.42755
 
1.2%
94.443333335
 
1.2%
955
 
1.2%
91.666666675
 
1.2%
90.278333334
 
1.0%
93.333333334
 
1.0%
98.147777784
 
1.0%
Other values (129)220
52.6%
(Missing)59
 
14.1%
ValueCountFrequency (%)
70.83251
0.2%
73.013333332
0.5%
752
0.5%
78.4761
0.2%
801
0.2%
ValueCountFrequency (%)
10092
22.0%
98.958752
 
0.5%
98.765555562
 
0.5%
98.752
 
0.5%
98.611251
 
0.2%

Saturday
Real number (ℝ≥0)

MISSING

Distinct109
Distinct (%)33.9%
Missing96
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean95.45384609
Minimum77.77666667
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:49.159605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum77.77666667
5-th percentile86.174225
Q192.62825893
median96.295
Q3100
95-th percentile100
Maximum100
Range22.22333333
Interquartile range (IQR)7.371741071

Descriptive statistics

Standard deviation4.429498899
Coefficient of variation (CV)0.04640461418
Kurtosis1.164433239
Mean95.45384609
Median Absolute Deviation (MAD)3.705
Skewness-1.086387768
Sum30736.13844
Variance19.6204605
MonotocityNot monotonic
2021-05-13T13:56:49.330850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10093
22.2%
91.666666677
 
1.7%
97.916666676
 
1.4%
91.66756
 
1.4%
96.42755
 
1.2%
92.55
 
1.2%
92.857142864
 
1.0%
954
 
1.0%
93.754
 
1.0%
98.214285714
 
1.0%
Other values (99)184
44.0%
(Missing)96
23.0%
ValueCountFrequency (%)
77.776666672
0.5%
81.993752
0.5%
83.3351
0.2%
84.622
0.5%
84.721666672
0.5%
ValueCountFrequency (%)
10093
22.2%
99.047142861
 
0.2%
98.752
 
0.5%
98.571428572
 
0.5%
98.4621
 
0.2%

Sunday
Real number (ℝ≥0)

MISSING

Distinct92
Distinct (%)35.9%
Missing162
Missing (%)38.8%
Infinite0
Infinite (%)0.0%
Mean94.73439318
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:49.533919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile85.71
Q192.466
median95.989375
Q3100
95-th percentile100
Maximum100
Range50
Interquartile range (IQR)7.534

Descriptive statistics

Standard deviation6.077321404
Coefficient of variation (CV)0.064151162
Kurtosis14.79243065
Mean94.73439318
Median Absolute Deviation (MAD)3.818
Skewness-2.983538347
Sum24252.00465
Variance36.93383545
MonotocityNot monotonic
2021-05-13T13:56:49.758316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10065
15.6%
96.4285714311
 
2.6%
91.6655
 
1.2%
98.412857145
 
1.2%
93.754
 
1.0%
92.54
 
1.0%
97.54
 
1.0%
954
 
1.0%
754
 
1.0%
95.577142863
 
0.7%
Other values (82)147
35.2%
(Missing)162
38.8%
ValueCountFrequency (%)
501
 
0.2%
66.672
0.5%
754
1.0%
801
 
0.2%
83.333333331
 
0.2%
ValueCountFrequency (%)
10065
15.6%
98.701428573
 
0.7%
98.611252
 
0.5%
98.571428572
 
0.5%
98.412857145
 
1.2%

Thursday
Real number (ℝ≥0)

MISSING

Distinct138
Distinct (%)45.1%
Missing112
Missing (%)26.8%
Infinite0
Infinite (%)0.0%
Mean94.22196663
Minimum57.5
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:49.990882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum57.5
5-th percentile82.17229167
Q191.666875
median95.758375
Q398.41145833
95-th percentile100
Maximum100
Range42.5
Interquartile range (IQR)6.744583333

Descriptive statistics

Standard deviation5.895095966
Coefficient of variation (CV)0.06256604672
Kurtosis5.525973135
Mean94.22196663
Median Absolute Deviation (MAD)3.827819444
Skewness-1.8286406
Sum28831.92179
Variance34.75215645
MonotocityNot monotonic
2021-05-13T13:56:50.194723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10073
 
17.5%
957
 
1.7%
94.4456
 
1.4%
95.833755
 
1.2%
96.6665
 
1.2%
91.6654
 
1.0%
91.666666674
 
1.0%
96.8754
 
1.0%
904
 
1.0%
93.754
 
1.0%
Other values (128)190
45.5%
(Missing)112
26.8%
ValueCountFrequency (%)
57.51
0.2%
74.1821
0.2%
752
0.5%
76.6662
0.5%
77.776666671
0.2%
ValueCountFrequency (%)
10073
17.5%
98.765555562
 
0.5%
98.4851
 
0.2%
98.43751
 
0.2%
98.333333331
 
0.2%

Tuesday
Real number (ℝ≥0)

MISSING

Distinct166
Distinct (%)40.8%
Missing11
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean95.19500538
Minimum61.666
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:50.512547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum61.666
5-th percentile86.91156944
Q193.44666667
median96.01
Q398.4375
95-th percentile100
Maximum100
Range38.334
Interquartile range (IQR)4.990833333

Descriptive statistics

Standard deviation4.89017773
Coefficient of variation (CV)0.05137010823
Kurtosis11.25529773
Mean95.19500538
Median Absolute Deviation (MAD)2.45
Skewness-2.478488807
Sum38744.36719
Variance23.91383823
MonotocityNot monotonic
2021-05-13T13:56:50.825152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10090
 
21.5%
93.7511
 
2.6%
95.833333337
 
1.7%
95.238571436
 
1.4%
96.8755
 
1.2%
87.55
 
1.2%
88.0955
 
1.2%
97.7784
 
1.0%
95.236666674
 
1.0%
96.6664
 
1.0%
Other values (156)266
63.6%
(Missing)11
 
2.6%
ValueCountFrequency (%)
61.6661
0.2%
651
0.2%
67.063333331
0.2%
75.2581
0.2%
79.631
0.2%
ValueCountFrequency (%)
10090
21.5%
99.166252
 
0.5%
98.982
 
0.5%
98.863751
 
0.2%
98.765555562
 
0.5%

Wednesday
Real number (ℝ≥0)

MISSING

Distinct120
Distinct (%)36.0%
Missing85
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean94.91694048
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:51.070571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile82.84125
Q192.855
median96.66666667
Q3100
95-th percentile100
Maximum100
Range50
Interquartile range (IQR)7.145

Descriptive statistics

Standard deviation7.137471218
Coefficient of variation (CV)0.07519702154
Kurtosis17.53841737
Mean94.91694048
Median Absolute Deviation (MAD)3.333333333
Skewness-3.472652892
Sum31607.34118
Variance50.94349538
MonotocityNot monotonic
2021-05-13T13:56:51.341225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100113
27.0%
96.666666676
 
1.4%
97.222222225
 
1.2%
97.777777785
 
1.2%
91.667142864
 
1.0%
504
 
1.0%
954
 
1.0%
964
 
1.0%
804
 
1.0%
93.333333334
 
1.0%
Other values (110)180
43.1%
(Missing)85
20.3%
ValueCountFrequency (%)
504
1.0%
73.3341
 
0.2%
77.4442
0.5%
78.031
 
0.2%
804
1.0%
ValueCountFrequency (%)
100113
27.0%
98.958751
 
0.2%
98.863751
 
0.2%
98.765555561
 
0.2%
98.4621
 
0.2%

weekbefore
Real number (ℝ≥0)

Distinct21
Distinct (%)5.1%
Missing3
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean95.17792771
Minimum33.33
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:51.535199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum33.33
5-th percentile75
Q193.33
median100
Q3100
95-th percentile100
Maximum100
Range66.67
Interquartile range (IQR)6.67

Descriptive statistics

Standard deviation9.941633267
Coefficient of variation (CV)0.1044531385
Kurtosis8.202619695
Mean95.17792771
Median Absolute Deviation (MAD)0
Skewness-2.618370844
Sum39498.84
Variance98.83607202
MonotocityNot monotonic
2021-05-13T13:56:51.734294image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
100308
73.7%
83.3313
 
3.1%
8013
 
3.1%
7512
 
2.9%
88.8912
 
2.9%
85.718
 
1.9%
907
 
1.7%
87.56
 
1.4%
505
 
1.2%
66.675
 
1.2%
Other values (11)26
 
6.2%
ValueCountFrequency (%)
33.331
 
0.2%
505
1.2%
601
 
0.2%
66.675
1.2%
71.433
0.7%
ValueCountFrequency (%)
100308
73.7%
94.441
 
0.2%
93.334
 
1.0%
92.861
 
0.2%
92.313
 
0.7%

ncoachingid
Real number (ℝ≥0)

MISSING

Distinct15
Distinct (%)4.8%
Missing106
Missing (%)25.4%
Infinite0
Infinite (%)0.0%
Mean3.496794872
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:51.915151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.871299825
Coefficient of variation (CV)0.821123323
Kurtosis4.893527961
Mean3.496794872
Median Absolute Deviation (MAD)1
Skewness2.04125621
Sum1091
Variance8.244362684
MonotocityNot monotonic
2021-05-13T13:56:52.037620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
281
19.4%
168
16.3%
360
14.4%
432
 
7.7%
517
 
4.1%
614
 
3.3%
712
 
2.9%
98
 
1.9%
106
 
1.4%
85
 
1.2%
Other values (5)9
 
2.2%
(Missing)106
25.4%
ValueCountFrequency (%)
168
16.3%
281
19.4%
360
14.4%
432
 
7.7%
517
 
4.1%
ValueCountFrequency (%)
164
1.0%
142
0.5%
131
 
0.2%
121
 
0.2%
111
 
0.2%

total coaching improved
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)3.1%
Missing162
Missing (%)38.8%
Infinite0
Infinite (%)0.0%
Mean2.375
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:52.157103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5.25
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.571810496
Coefficient of variation (CV)0.6618149457
Kurtosis1.422620542
Mean2.375
Median Absolute Deviation (MAD)1
Skewness1.31068121
Sum608
Variance2.470588235
MonotocityNot monotonic
2021-05-13T13:56:52.264284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
199
23.7%
264
 
15.3%
340
 
9.6%
427
 
6.5%
513
 
3.1%
76
 
1.4%
65
 
1.2%
82
 
0.5%
(Missing)162
38.8%
ValueCountFrequency (%)
199
23.7%
264
15.3%
340
9.6%
427
 
6.5%
513
 
3.1%
ValueCountFrequency (%)
82
 
0.5%
76
 
1.4%
65
 
1.2%
513
3.1%
427
6.5%

csat
Real number (ℝ≥0)

Distinct219
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.86746411
Minimum79.26
Maximum99.22
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:52.433122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum79.26
5-th percentile91.947
Q193.97
median95.085
Q396.0625
95-th percentile97.16
Maximum99.22
Range19.96
Interquartile range (IQR)2.0925

Descriptive statistics

Standard deviation1.833150053
Coefficient of variation (CV)0.01932327453
Kurtosis12.43087158
Mean94.86746411
Median Absolute Deviation (MAD)1.095
Skewness-1.910173617
Sum39654.6
Variance3.360439117
MonotocityNot monotonic
2021-05-13T13:56:52.658838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.417
 
1.7%
95.786
 
1.4%
94.785
 
1.2%
95.475
 
1.2%
95.524
 
1.0%
94.644
 
1.0%
95.854
 
1.0%
96.014
 
1.0%
94.084
 
1.0%
95.584
 
1.0%
Other values (209)371
88.8%
ValueCountFrequency (%)
79.261
0.2%
88.751
0.2%
88.831
0.2%
89.131
0.2%
89.611
0.2%
ValueCountFrequency (%)
99.222
0.5%
98.461
0.2%
98.141
0.2%
982
0.5%
97.992
0.5%

actualvalue
Real number (ℝ≥0)

Distinct74
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.36593301
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Memory size3.4 KiB
2021-05-13T13:56:52.918125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile85.4535
Q191.3
median95.74
Q3100
95-th percentile100
Maximum100
Range50
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation5.818415338
Coefficient of variation (CV)0.06165800678
Kurtosis9.862377143
Mean94.36593301
Median Absolute Deviation (MAD)4.26
Skewness-2.152489284
Sum39444.96
Variance33.85395704
MonotocityNot monotonic
2021-05-13T13:56:53.194325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100116
27.8%
9014
 
3.3%
94.4411
 
2.6%
88.8911
 
2.6%
91.6710
 
2.4%
92.3110
 
2.4%
90.919
 
2.2%
96.679
 
2.2%
96.888
 
1.9%
95.658
 
1.9%
Other values (64)212
50.7%
ValueCountFrequency (%)
501
0.2%
66.672
0.5%
751
0.2%
76.922
0.5%
77.782
0.5%
ValueCountFrequency (%)
100116
27.8%
98.253
 
0.7%
98.042
 
0.5%
97.874
 
1.0%
97.671
 
0.2%
Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
0
222 
1
196 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0222
53.1%
1196
46.9%
2021-05-13T13:56:53.722331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-05-13T13:56:53.877412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0222
53.1%
1196
46.9%

Most occurring characters

ValueCountFrequency (%)
0222
53.1%
1196
46.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number418
100.0%

Most frequent character per category

ValueCountFrequency (%)
0222
53.1%
1196
46.9%

Most occurring scripts

ValueCountFrequency (%)
Common418
100.0%

Most frequent character per script

ValueCountFrequency (%)
0222
53.1%
1196
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII418
100.0%

Most frequent character per block

ValueCountFrequency (%)
0222
53.1%
1196
46.9%

Interactions

2021-05-13T13:56:03.424765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:03.587787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:03.769466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:03.946445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:04.098518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:04.246680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:04.452308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:04.594823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:04.732675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:04.862346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:05.034724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:05.179827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:05.321603image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:05.591084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:05.730303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:05.891562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:06.060134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:06.237071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:06.480083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:06.646099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:06.830515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:07.029061image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:07.298799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:07.487819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:07.704865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:07.935016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:08.119119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:08.286464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:08.493360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:08.670672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:08.894155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:09.084914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:09.278440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:09.443418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:09.627321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:09.900505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:10.104686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:10.290270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:10.477304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:10.648899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:10.830376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:11.144989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:11.355391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:11.521799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:11.678759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:11.826632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:11.972758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:12.122107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:12.317005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:12.478855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:12.731893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:12.929704image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:13.124404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:13.364231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:13.603709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:13.893592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:14.087528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:14.275223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:14.442372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:14.679144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:14.845110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:14.985579image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:15.258393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:15.487567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:15.676042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:15.862100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:16.017682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:16.180714image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:16.345496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:16.522244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:16.690531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:16.848489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:16.983641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:17.153905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:17.306733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:17.456278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:17.616162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:17.760773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:17.903835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:18.046285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:18.190742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:18.321480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:18.470949image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:18.630759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:18.796556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:18.936471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:19.115747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:19.276955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:19.429418image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:19.683552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:19.824468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:19.976398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:20.130966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:20.272000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:20.399567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:20.534255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:20.684194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:20.844315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:20.969045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:21.092604image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:21.245793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:21.382823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:21.502300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:21.628632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:21.755870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:21.898684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:22.078583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:22.223810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:22.376216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:22.520554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:23.603206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:23.738058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:23.893979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:24.092119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:24.245448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:24.384771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:24.522537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:24.685341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:24.833017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:24.968540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:25.101727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:25.265261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:25.410709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:25.541251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:25.715356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:25.888513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:26.071655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:26.205643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:26.329941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:26.472189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:26.593222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:26.728134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:26.855878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:26.983873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:27.093727image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:27.234112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:27.413081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:27.541646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:27.673569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:27.819995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:27.964411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:28.097776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:28.235290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:28.377314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:28.572280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:28.701143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:28.858344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:28.989281image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:29.144236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:29.339962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:29.527069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:29.674320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:29.856313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:30.061033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:30.237193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:30.400747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:30.575897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:30.758924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:30.944692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:31.098018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:31.213726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:31.337812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:31.506763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:31.693289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:31.907154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:32.089226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:32.253881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:32.428712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:32.848373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:34.109959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:34.865816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:35.192598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:35.430855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:35.765915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:36.027998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:36.208474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:36.425343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:36.756101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:36.961748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:37.133577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:37.284976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:37.473029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:37.718929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:37.942327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:38.143616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:38.319360image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:38.565921image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:38.766097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:38.920183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:39.365194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:39.661017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:39.991111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:40.310224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:40.548694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:40.688298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:40.845145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:41.016592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:41.206721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:41.402495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:41.594646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:41.777632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:41.981774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:42.197581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:42.324421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:42.466371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:42.675449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:42.836038image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:43.065457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:43.227943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-13T13:56:43.376047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-13T13:56:54.102104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-13T13:56:54.461529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-13T13:56:54.858189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-13T13:56:55.191198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-13T13:56:43.894180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-13T13:56:44.448692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-13T13:56:44.855694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-13T13:56:45.155537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexUnnamed: 0employeeidFridayMondaySaturdaySundayThursdayTuesdayWednesdayweekbeforencoachingidtotal coaching improvedcsatactualvalueactualvalue_class
0332670250791.46800091.880000100.00000091.112000NaN96.666000NaN80.0NaNNaN95.5290.000
1442670250791.46800091.880000100.00000091.112000NaN96.666000NaN100.0NaNNaN95.5290.000
27726702537100.00000088.527143100.00000095.832857NaN95.713750NaN100.01.01.096.6388.890
38826702537100.00000088.527143100.00000095.832857NaN95.713750NaN100.01.01.096.6388.890
4101026702549100.00000098.14777892.85714396.428571NaN96.875000100.000000100.01.01.097.9980.000
5111126702549100.00000098.14777892.85714396.428571NaN96.875000100.00000080.01.01.097.9980.000
6141426702594100.00000094.44500089.286667NaN93.33333395.101429100.000000100.01.0NaN96.19100.001
715152670261195.303333100.000000NaNNaN97.28111197.22222298.765556100.02.0NaN95.14100.001
816162670261796.98428691.11111195.22142995.328571NaN97.601111NaN100.0NaNNaN93.8797.061
917172670261796.98428691.11111195.22142995.328571NaN97.601111NaN100.0NaNNaN93.8797.061

Last rows

df_indexUnnamed: 0employeeidFridayMondaySaturdaySundayThursdayTuesdayWednesdayweekbeforencoachingidtotal coaching improvedcsatactualvalueactualvalue_class
4084784782670397393.333333100.000000NaNNaN90.00000096.42750078.030000100.00NaNNaN96.97100.001
4094794792670397790.177500100.000000NaNNaN86.00000061.66600092.666000100.00NaNNaN79.2692.861
41048048026703978100.000000100.000000NaNNaN96.666000100.00000073.334000100.00NaNNaN96.9488.240
4114814812670397993.33333396.427500NaNNaN96.427500100.000000100.000000100.00NaNNaN97.46100.001
4124834832670398293.75000094.443333NaNNaN82.66600095.55600091.667500100.00NaNNaN97.0193.180
4134844842670398293.75000094.443333NaNNaN82.66600095.55600091.667500100.00NaNNaN97.0193.180
4144854852670398383.33500095.832500NaNNaN100.000000100.000000100.000000100.00NaNNaN91.5488.891
41548648635706305NaN92.000000NaN93.33479.16750087.50000091.667500100.005.04.095.9587.500
4164874873570631781.770000NaN81.99375NaN87.21333386.83777886.64888933.3316.04.096.6888.461
4174884883570631781.770000NaN81.99375NaN87.21333386.83777886.64888950.0016.04.096.6888.461